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Record W4385828171 · doi:10.5964/ps.12017

What Constitutes Successful Goal Pursuit? Exploring the Relation Between Subjective and Objective Measures of Goal Progress

2023· article· en· W4385828171 on OpenAlexafffund
Aidan Smyth, Marina Milyavsksaya, Malte Friese, Kaitlyn M. Werner, Marie-Lena Frech, David D. Loschelder, Joanne Anderson, Michael Inzlicht, Marta Kolbuszewska, Kelly Wang

Bibliographic record

VenuePersonality Science · 2023
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of TorontoCarleton University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsGoal pursuitGoal settingGoal orientationRelation (database)PsychologyComputer scienceSocial psychologyData mining

Abstract

fetched live from OpenAlex

Despite a rich literature on goals, the notion of successful goal pursuit remains somewhat unclear. Most research on personal goal pursuit relies on subjective measures of goal progress and research that uses objective measures (e.g., grade point average) often ignores individuals’ idiosyncratic goals. The present research investigated the relation between diverse measures of goal progress in the context of academic and weight loss goals using four datasets (total sample = 351). Overall, subjective measures were positively related to objective measures. The magnitudes of these associations varied across studies and were generally smaller than would be expected if the measures assessed the same construct (Rβ = .05–.39). These findings suggest that subjective and objective measures may reflect related but distinct constructs. The present research draws attention to an important topic in the goals literature and highlights the need for additional research on the conceptualization and operationalization of successful goal pursuit.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.904

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.198
GPT teacher head0.423
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations14
Published2023
Admission routes2
Has abstractyes

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